def get_prob(Dataset_train, Dataset_test, similarity_measure):
"""
Generate predict results, given two datasets and similarity_measure
This is the first predict scheme, without using any machine learning techniques
Return a list of predicted scores
"""
data_train = Dataset_train
data_test = Dataset_test
# placeholder for final prediction results
pred = []
# get the sizes for further usages
train_size = len(data_train.data)
test_size = len(data_test.data)
category_scores_overall = []
# using utility to get a n*m array of similarity scores
sim_result = similarity_utils.iterate_combination_2d_sim(data_test.data, data_train.data, similarity_measure)
# t_a = (time() - t0)
# joblib.dump(sim_result, './'+str(t_a)+'.pkl')
# print sim_result
for i in range(test_size):
# list for holding sum of scores on each category
# note: scores in this list are not averaged but sum
category_scores = [0] * len(set(data_train.target))
# get a list of sim results from ith row in the sim_result 2d array
# i.e. sim scores for one item in the test set compared to all train sentencs
score_list = sim_result[i]
# iterate over the score_list and sum up all scores by their categories into category_scores
for j in range(train_size):
# j's category
j_cate = data_train.target[j]
# j's sim score
j_score = score_list[j]
# add score by category back to the score sum list
category_scores[j_cate] += j_score
# after adding all scores to category score list, first average
for category in set(data_train.target):
# numpy array to list, otherwise cannot count
# train_targets_list = map(None, data_train.target)
train_targets_list = data_train.target.tolist()
# occurance of this category in training set
occurance = train_targets_list.count(category)
# average score in the list
category_scores[category] = category_scores[category] / float(occurance)
category_scores_overall.append(category_scores)
# convert to numpy array
pred_array = np.array(category_scores_overall)
return pred_array
# log file
logFile = open('mylogfile.txt', 'wb')
list1 = similarity_utils.load_sentences('data_not_sell')
list2 = similarity_utils.load_sentences('data_sell_share')
print "len(list1):", len(list1)
print "len(list2):", len(list2)
sentence1 = list1[1]
sentence2 = list2[2]
# test similarity from sentence
score1 = similarity_overlap.sim_overlap(sentence1, sentence2)
print "sim(list1[1], list2[2]) :", score1
# test iterate_combination_2d_sim
score_array = similarity_utils.iterate_combination_2d_sim(list1, list2, similarity_overlap.sim_overlap)
print "score_array[1][2] :", score_array[1][2]
# Test for combined_list
combined_list = list1+list2
# Sim_overlap
score_array_overlap = similarity_utils.iterate_combination_2d_sim(combined_list, combined_list, similarity_overlap.sim_overlap)
# pretty print
score_array_overlap = [[similarity_utils.PrettyFloat(n) for n in row] for row in score_array_overlap]
pprint(score_array_overlap, logFile)
logFile.write('\n')
# Sim_overlap_idf
score_array_idf = similarity_utils.iterate_combination_2d_sim(combined_list, combined_list, similarity_overlap_idf.sim_overlap_idf)
